You ll make the architecture and security calls yourself, lead the engineers who build against them, and be the technical voice on AI to the company s senior leaders. You own turning an AI idea into a governed, production-grade system - not just the prototype.

ESSENTIAL DUTIES AND RESPONSIBILITIES

AI Architecture & Technical Strategy

- Own architecture decisions for AI features on AIS s platform: LLM/model selection, RAG and agentic design, vector store and retrieval strategy, MCP-based integration
- Set reusable design standards - prompt/context management, evaluation, guardrails - so features aren t built as one-offs
- Make build-vs-buy and vendor calls (OpenAI, Anthropic, Azure OpenAI, automation platforms) on cost, latency, accuracy, and lock-in

Zero-to-One Delivery & Scaling

- Take AI ideas from prototype to a production system that holds up under real case volumes and audit scrutiny
- Define AIS s bar for production-ready : accuracy thresholds, latency budgets, human-in-the-loop fallback, monitoring, rollback plans
- MOwn the path from single-workflow pilot to full-population, multi-client rollout, including the data pipeline and infra work scale requires

Security, Compliance & Governance

- Own security and privacy posture for every AI system touching client data, consumer PII, and case information - access controls, encryption, vendor data-handling terms, retention
- Build and enforce an AI governance framework: model risk classification, human-oversight checkpoints, audit logging, explainability
- Partner with compliance, legal, and security (internal and client-side) so AI features clear regulatory bar before shipping

Engineering Leadership

- Hire,



mentor, and set the technical bar for a team of AI/GenAI engineers and automation developers; review architecture and code at key checkpoints
- Turn ambiguous business asks into scoped workstreams with clear ownership and definition of done; run delivery across concurrent initiatives

Stakeholder Communication

- Be the primary technical voice on AI to the senior leaders - explain trade-offs, risk, and ROI in terms an executive audience can act on
- Collaborate and defend roadmap materials (capability-to-lifecycle mapping, Now/Next/Later views); support client/RFP conversations where AI is a differentiator
- Push back on scope or timeline when the technical reality doesn t match the ask

MLOps & Production Operations

- Own monitoring, drift tracking, incident response, and continuous evaluation for every deployed AI feature
- Track the metrics that matter per feature (accuracy, cost per transaction, latency, escalation rate) and drive iteration on them

SKILLS
- 6+ years in software/AI engineering, including 4+ years hands-on GenAI/LLM or applied ML, with proven ownership of architecture - not just implementation - for production AI systems
- Track record taking at least one AI/automation product from concept to production at meaningful scale




- Hands-on with LLM integration (OpenAI, Anthropic/Claude, Azure OpenAI), RAG and vector databases, and agentic/orchestration frameworks (LangChain, LangGraph or similar), including MCP-based integration
- Experience owning security, data governance, or compliance posture for AI systems in a regulated or sensitive-data environment (financial services, healthcare, insurance strongly preferred)
- Demonstrated people leadership - has hired, mentored, and directly managed engineers, not just coordinated a project team
- Direct experience presenting to senior/executive stakeholders - roadmaps, architecture decisions, or vendor evaluations at that level
- Prior experience in fintech, lending, banking operations & servicing, insurance, or another regulated financial-services domain
- Experience building or governing an AI Center of Excellence, model risk framework, or Responsible AI program
- Experience automating compliance-adjacent workflows (call/quality monitoring, document review, claims processing) via integration with enterprise systems

QUALIFICATIONS
- Bachelor s or Master s in Computer Science, AI/ML, Data Engineering, or related field.
- Relevant certification (Google Cloud, Microsoft Azure AI, or equivalent)
- Robust fundamentals: Python and/or C#/.NET, REST APIs, cloud (Azure/AWS/GCP), Docker/Kubernetes, CI/CD

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI & Automation Lead / Manager (Gurugram)
🏢 AIS business solution
📍 Gurugram